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Automatic Evaluation of Street-Level Walkability Based on Computer Vision Techniques and Urban Big Data

A Case Study of Kowloon West, Hong Kong

  • Conference paper
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Intelligence for Future Cities (CUPUM 2023)

Part of the book series: The Urban Book Series ((UBS))

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Abstract

The walkability of an urban environment is a critical aspect of urban design and planning, and has a direct impact on the quality of life for residents. Therefore, it is essential to conduct a systematic evaluation of the pedestrian environment to improve the walkability of a city. In recent years, there has been a growing emphasis on the application of automated evaluation methods, incorporating artificial intelligence and urban big data analysis. This study proposes a systematic walkability evaluation index with automated measurement capabilities, and corresponding measurement pipelines utilizing computer vision techniques as well as urban big data. To demonstrate the utility of the proposed index and measurement methods, this study conducts a systematic measurement of street-level walkability in the Kowloon West of Hong Kong as a case study.

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References

  • Abdulla KM, Abdelmonem MG, Selim G (2017) Walkability in historic urban spaces: testing the safety and security in Martyrs’ Square in Tripoli. Int J Archit Res Archnet-IJAR 11(3):163–177

    Article  Google Scholar 

  • Aghaabbasi M, Moeinaddini M, Shah MZ, Asadi-Shekari Z (2017) A new assessment model to evaluate the microscale sidewalk design factors at the neighbourhood level. J Transp Health 5:97–112

    Article  Google Scholar 

  • Al Shammas T, Escobar F (2019) Comfort and time-based walkability index design: a GIS-based proposal. Int J Environ Res Public Health 16(16):2850

    Article  Google Scholar 

  • Alves F, Cruz S, Rother S, Strunk T (2021) An application of the walkability index for elderly health—WIEH. The case of the UNESCO historic centre of Porto, Portugal. Sustainability 13(9):4869

    Google Scholar 

  • Baidu Map Open Platform (2022) Static panorama. Retrieved 08 10, 2022, from https://lbsyun.baidu.com/index.php?title=viewstatic

  • Bartzokas-Tsiompras A, Photis YN (2020) Does neighborhood walkability affect ethnic diversity in Berlin? Insights from a spatial modeling approach. Europ J Geogr 11(1)

    Google Scholar 

  • Belza B, Altpeter M, Smith ML, Ory MG (2017) The healthy aging research network: modeling collaboration for community impact. Am J Prev Med 52(3):228-S232

    Article  Google Scholar 

  • Boarnet MG, Day K, Alfonzo M, Forsyth A, Oakes M (2006) The Irvine-Minnesota inventory to measure built environments: reliability tests. Am J Prev Med 30(2):153–159

    Article  Google Scholar 

  • Brownson RC, Brennan Ramirez LK, Hoehner CM, Cook RA (2003) Analytic audit tool and checklist audit tool.

    Google Scholar 

  • Cheng B, Misra I, Schwing AG, Kirillov A, Girdhar R (2022) Masked-attention mask transformer for universal image segmentation. CVPR

    Google Scholar 

  • Cheng B, Schwing AG, Kirillov A (2021) Per-pixel classification is not all you need for semantic segmentation. NeurIPS

    Google Scholar 

  • Coalition SA (2012) Streets for people: compendium for south Australian practice

    Google Scholar 

  • Cooper CH, Chiaradia AJ (2020) sDNA: 3-d spatial network analysis for GIS, CAD. Command Line Python Softwarex 12:100525

    Google Scholar 

  • Day K, Boarnet M, Alfonzo M, Forsyth A (2006) The Irvine-Minnesota inventory to measure built environments: development. Am J Prev Med 30(2):144–152

    Article  Google Scholar 

  • De Nadai M, Vieriu RL, Zen G, Dragicevic S, Naik N, Caraviello M, … Lepri B (2016) Are safer looking neighborhoods more lively? A multimodal investigation into urban life. In: Proceedings of the 24th ACM international conference on Multimedia, 1127–1135

    Google Scholar 

  • Diakoulaki D, Mavrotas G, Papayannakis L (1995) Determining objective weights in multiple criteria problems: The critic method. Comput Oper Res 22(7):763–770

    Article  MATH  Google Scholar 

  • Dubey A, Naik N, Parikh D, Raskar R, Hidalgo CA (2016) Deep learning the city: quantifying urban perception at a global scale. European conference on computer vision, Springer(Cham), 196–212

    Google Scholar 

  • Emery J, Crump C, Bors P (2003) Reliability and validity of two instruments designed to assess the walking and bicycling suitability of sidewalks and roads. Am J Health Promot 18(1):38–46

    Article  Google Scholar 

  • Evenson KR, Sotres-Alvarez D, Herring AH, Messer L, Laraia BA, Rodríguez DA (2009) Assessing urban and rural neighborhood characteristics using audit and GIS data: Derivation and reliability of constructs. Int J Behav Nutr Phys Act 6(1):44

    Article  Google Scholar 

  • Gebel K, Bauman A, Owen N (2009) Correlates of non-concordance between perceived and objective measures of walkability. Ann Behav Med 37(2):228–238

    Article  Google Scholar 

  • Gehl J (1987) Life between buildings. Van Nostrand Reinhold, New York

    Google Scholar 

  • Gehl Institute (2016) PublicLifeDiversityToolkit_2.0

    Google Scholar 

  • Google Maps Platform (2022) Google maps coverage. Retrieved 08 10, 2022, from https://developers.google.com/maps/coverage

  • Goolge Maps Platform (2022) Pricing that scales to fit your needs. Retrieved 08 10, 2022, from https://mapsplatform.google.com/pricing/

  • Hooper P, Knuiman M, Bull F, Jones E, Giles-Corti B (2015) Are we developing walkable suburbs through urban planning policy? Identifying the mix of design requirements to optimise walking outcomes from the ‘Liveable Neighbourhoods’ planning policy in Perth, Western Australia. Int J Behav Nutr Phys Act 12(1):1–11

    Article  Google Scholar 

  • Jacobs J (1961) The death and life of great american cities. Vintage, New York

    Google Scholar 

  • Jacobs J (1992) The death and life of great american cities. Vintage, New York

    Google Scholar 

  • Jocher G, Chaurasia A, Stoken A, Borovec J, NanoCode012, Kwon Y, …Minh M T. (2022) ultralytics/yolov5: v6.1—TensorRT. TensorFlow Edge TPU and OpenVINO Export and Inference, https://doi.org/10.5281/zenodo.6222936

  • Kim J, Park D, Suh Y, Jung D (2019) Development of sidewalk block pavement condition index (SBPCI) using analytical hierarchy process. Sustainability 11(24):7086

    Article  Google Scholar 

  • Koo BW, Guhathakurta S, Botchwey N (2022a) Development and validation of automated microscale walkability audit method. Health Place 73:102733

    Article  Google Scholar 

  • Koo BW, Guhathakurta S, Botchwey N (2022b) How are neighborhood and street-level walkability factors associated with walking behaviors? a big data approach using street view images. Environ Behav 54(1):211–241

    Article  Google Scholar 

  • Kostanjsek J, Lipar P (2007) Pedestrian crossings priority for pedestrian safety. 3rd Urban Street Symposium: Uptown, Downtown, or Small Town: Designing Urban Streets That WorkTransportation Research BoardInstitute of Transportation Engineers (ITE) US Access Board

    Google Scholar 

  • Li Y, Yabuki N, Fukuda T, Zhang J (2020) A big data evaluation of urban street walkability using deep learning and environmental sensors-a case study around Osaka University Suita campus. eCAADe, 2, 319–328

    Google Scholar 

  • Limgomonvilas T, Nimanong R (2018) Multi-criteria analysis and network analysis for walkability score in Amphoe Muang, Nonthaburi, Thailand. J Adv Res Soc Sci Humanities 3(4):125–135

    Google Scholar 

  • Lin T-Y, Maire M, Belongie S, Bourdev L, Girshick R, Hays J, … Dollár P (2014) Microsoft COCO: common objects in context. Computer Vision–ECCV 2014: 13th European Conference, Springer International Publishing, pp 740–755

    Google Scholar 

  • Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s. Proceedings of the IEEE/cvf conference on computer vision and pattern recognition

    Google Scholar 

  • Millstein AR, Cain KL, Sallis JF, Conway TL, Geremia C, Frank LD, Saelens BE (2013) Development, scoring, and reliability of the Microscale Audit of Pedestrian Streetscapes (MAPS). BMC Public Health 13(1:1–15

    Google Scholar 

  • Ng S, Lai C, Liao P, Lao M, Lau W (2016) Measuring and improving walkability in Hong Kong

    Google Scholar 

  • Ogawa Y, Oki T, Zhao C, Sekimoto Y, Shimizu C (2022) Evelopment of a model for evaluating subjective impressions of streetscapes using omnidirectional street image big data. Okinawa: The 31st annual conference of geographical information systems association

    Google Scholar 

  • Oki T, Kizawa S (2022) Model for evaluating impression of streets in residential areas based on image big data and a large questionnaire survey. J Archit Planning 87(800):2102–2113

    Article  Google Scholar 

  • Petritsch TA, Landis BW, McLeod PS, Huang HF, Challa S, Guttenplan M (2005) Level-of-service model for pedestrians at signalized intersections. Transp Res Rec 1939(1):54–62

    Article  Google Scholar 

  • Salaryexplorer (2022) Salaryexplorer. Retrieved 08 10, 2022, from http://www.salaryexplorer.com/salary-survey.php?loc=97&loctype=1#:~:text=The%20average%20hourly%20wage%20%28pay%20per%20hour%29%20in,into%20two%20categories%3A%20salaried%20jobs%20and%20hourly%20jobs

  • Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, 618–626

    Google Scholar 

  • Tchinda PE, Kim SN (2020) The paradox of “eyes on the street”: pedestrian density and fear of crime in Yaoundé. Cameroon. Sustainability 12(13):5300

    Article  Google Scholar 

  • Transport for London (2018) Walking action plan: Making London the world’s most walkable city

    Google Scholar 

  • Wang H, Silva EA, Liu L (2022) Large‐scale evaluation of the urban street view with deep learning method. Machine learning and the city: applications in architecture and urban design, 495–499

    Google Scholar 

  • Wilson JQ, Kelling GL (1982) Broken windows. Atlantic Monthly 249(3):29–38

    Google Scholar 

  • Yin L (2017) Street level urban design qualities for walkability: combining 2D and 3D GIS measures. Comput Environ Urban Syst 64:288–296

    Article  Google Scholar 

  • Yin L, Wang Z (2016) Measuring visual enclosure for street walkability: using machine learning algorithms and Google Street View imagery. Appl Geogr 76:147–153

    Article  Google Scholar 

  • Zhou H, He S, Cai Y, Wang M, Su S (2019) Social inequalities in neighborhood visual walkability: Using street view imagery and deep learning technologies to facilitate healthy city planning. Sustain Cities Soc 50:101605

    Article  Google Scholar 

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Acknowledgements

This work was supported by JST SPRING under Grant Number JPMJSP2106, and JSPS KAKENHI under Grant Number JP22K04490. We are grateful for the invaluable feedback provided by anonymous reviewers, which significantly enhanced the quality of this paper. Our sincere thanks to all involved parties.

Author information

Authors and Affiliations

  1. School of Environment and Society, Tokyo Institute of Technology, Tokyo, Japan

    Lu Huang & Takuya Oki

  2. Center for Real Estate Innovation, The University of Tokyo, Tokyo, Japan

    Sachio Muto & Hongjik Kim

  3. Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan

    Yoshiki Ogawa & Yoshihide Sekimoto

Authors
  1. Lu Huang
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  2. Takuya Oki
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  3. Sachio Muto
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  4. Hongjik Kim
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  5. Yoshiki Ogawa
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  6. Yoshihide Sekimoto
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Corresponding author

Correspondence to Lu Huang .

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Editors and Affiliations

  1. Taubman College of Architecture and Urban Planning, University of Michigan, Ann Arbor, MI, USA

    Robert Goodspeed

  2. Department of Geography, McGill University, Montreal, QC, Canada

    Raja Sengupta

  3. Department of Built Environment, Aalto University, Aalto, Finland

    Marketta Kyttä

  4. City Futures Research Centre, University of New South Wales, Sydney, NSW, Australia

    Christopher Pettit

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Huang, L., Oki, T., Muto, S., Kim, H., Ogawa, Y., Sekimoto, Y. (2023). Automatic Evaluation of Street-Level Walkability Based on Computer Vision Techniques and Urban Big Data. In: Goodspeed, R., Sengupta, R., Kyttä, M., Pettit, C. (eds) Intelligence for Future Cities. CUPUM 2023. The Urban Book Series. Springer, Cham. https://doi.org/10.1007/978-3-031-31746-0_13

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  • DOI: https://doi.org/10.1007/978-3-031-31746-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31745-3

  • Online ISBN: 978-3-031-31746-0

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